System and method for predictive modeling in disease management

ABSTRACT

A method and system for administering a disease management program to improve healthcare quality, reduce healthcare costs, and optimize delivery of healthcare services. A multi-condition risk assessment is conducted for all or a substantial portion of a population of program participants, and collected multi-condition risk assessment data are combined with claims data for predictive modeling of future healthcare risk and expense. Participants are risk-stratified into one or more classifications of future healthcare cost risk, and appropriate intervention or delivery of healthcare services is made based on the risk classification.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. Non-Provisional patentapplication, entitled “Predictive Modeling System and Method for DiseaseManagement”, Ser. No. 11/200,804, filed Aug. 10, 2005, the entirety ofwhich is hereby incorporation herein by reference for all purposes.

TECHNICAL FIELD

The present invention relates generally to the field of diseasemanagement, and more particularly to a system and method ofidentification, validation and risk stratification utilizing predictivemodeling in combination with a multi-condition risk assessment tool.

BACKGROUND OF THE INVENTION

Disease management is an approach to patient care and medical costcontrol that is based on the premise that a minority of healthcare usersgenerate the majority of total healthcare costs. Disease managementprograms use information technology to identify individuals who have orare at risk for various adverse medical conditions. Disease managementprograms offer customized education and clinical support to helpindividuals take more responsibility for self-care, improve theirhealth, and avoid expensive medical events down the road. By proactivelyimproving the health of the minority of a population that consumes themajority of healthcare resources, disease management programs cansignificantly reduce many preventable medical expenses, includinghospitalization and ER visits.

Disease management programs commonly segment an overall population ofhealthcare consumers within a program into multiple risk categories, andprovide varying levels of monitoring and care to individual programparticipants depending on their risk categorization. This is variouslytermed “risk stratification” or “predictive modeling.” Preemptiveinterventions triggered by a high-risk stratification will, on average,increase the quality of care, reduce adverse clinical events, andaccordingly reduce paid claims dollars. To distinguish high-risk personsfrom low-risk persons, traditional identification and predictivemodeling programs have typically utilized a retrospective claimsanalysis method that partitions the population based on prior medicalutilization or historical health plan claims data, and then sets carelevels appropriately.

Predictive modeling based solely on retrospective claims data is oflimited accuracy in estimating future risk, however, and therefore needsexist for continued improvement in the field of disease managementidentification and risk stratification. It is to the provision ofimproved systems and methods of disease management that the presentinvention is primarily directed.

SUMMARY OF THE INVENTION

The present invention provides improved systems and methods ofidentification and risk stratification for disease management. Inexample forms, the present invention incorporates a multi-condition riskassessment to collect data elements from all or a substantial portion ofprogram participants within an identified population. The collectedmulti-condition risk assessment data is then combined with analysis ofmedical claims data, resulting in substantially improved accuracy inestimating future risk, and more effective targeting of education,clinical support, and intervention resources within a disease managementprogram. In this manner, a more efficient allocation of resources may berealized, to provide better and more efficient patient care and greatercost reductions than typical programs.

In one aspect, the present invention is a method of disease management,preferably including the steps of defining a population of programparticipants based on claims data filtering, conducting amulti-condition risk assessment of all or substantially all programparticipants in the defined population, combining data gathered fromthat multi-condition risk assessment with claims data for each programparticipant in the defined population, and classifying future healthcarerisk of the program participants based on the combination of claims dataand data gathered from the multi-condition risk assessment.

In another aspect, the invention is a method of disease management,preferably including the steps of conducting a multi-condition riskassessment of each program participant within a defined population,generating a patient record for each program participant in thepopulation, and providing differing degrees of healthcare interventionfor program participants based on their patient record.

In still another aspect, the invention is a method of optimizingdelivery of healthcare services to participants in a disease managementprogram. The method preferably includes conducting a multi-conditionrisk assessment of at least a substantial portion of a definedpopulation, categorizing individual program participants selected fromthe population into at least a higher-risk category and a lower-riskcategory, and providing more intensive healthcare intervention toprogram participants categorized in the higher-risk category and lessintensive healthcare intervention to program participants categorized inthe lower-risk category.

These and other aspects, features and advantages of the invention willbe understood with reference to the detailed description herein, andwill be realized by means of the various elements and combinationsparticularly pointed out in the appended claims. It is to be understoodthat both the foregoing general description and the following detaileddescription of the invention are exemplary and explanatory of preferredembodiments of the invention, and are not restrictive of the invention,as claimed.

BRIEF DESCRIPTION OF THE DRAWING FIGURE

The drawing FIGURE shows schematically a system and method of diseasemanagement incorporating a multi-condition risk assessment, according toan example form of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The present invention may be understood more readily by reference to thefollowing detailed description of the invention. It is to be understoodthat this invention is not limited to the specific devices, methods,conditions or parameters described herein, and that the terminology usedherein is for the purpose of describing particular embodiments by way ofexample only and is not intended to be limiting of the claimedinvention. Also, as used in the specification including the appendedclaims, the singular forms “a,” “an,” and “the” include the plural, andreference to a particular numerical value includes at least thatparticular value, unless the context clearly dictates otherwise. Rangesmay be expressed herein as from “about” or “approximately” oneparticular value and/or to “about” or “approximately” another particularvalue. When such a range is expressed, another embodiment includes fromthe one particular value and/or to the other particular value.Similarly, when values are expressed as approximations, by use of theantecedent “about,” it will be understood that the particular valueforms another embodiment.

Preferred and example forms of the disease management system and methodof the present invention improve upon the effectiveness of previouslyknown systems and methods by combining predictive modeling based onretrospective claims data with information gathered from amulti-condition risk assessment (“MCRA”) of program participants withina defined population. For example, one or more of the following dataelements is/are typically extracted from retrospective claims data forpatients participating in a disease management plan: (1) date that ahealthcare service was performed; (2) place the service was performed;(3) type of service that was performed; (4) medical diagnoses; (5) theoccurrence of lab testing, and/or lab test results; (6) procedurescarried out; (7) medical codes for services performed; (8) pharmacyprescriptions and codes; (9) amount the insurance carrier paid; (10)type of provider that performed the service (e.g., cardiologist,emergency room physician, etc.); and/or (11) dates of admission anddischarge for inpatient claims.

Because such data is gathered from prior claims, predictive modeling istypically based on data collected from those program participants whohave received medical attention resulting in an insurance claim, andparticipants not incurring claims typically do not contribute data usedfor predictive modeling under previously known systems and methods ofdisease management. Accordingly, previously known predictive modelingprocesses may help identify program participants with chronic conditionsthat have required some level of medical care in the past and are morelikely to generate high medical costs for related conditions in thefuture, but such a predictive modeling process alone typically cannotidentify higher-risk participants who have not yet sought treatment andincurred claims, nor does it optimally reflect risk for others whoseclaims underestimate disease burden (such as participants who have beennon-compliant with prescribed treatment programs and therefore have notincurred claims that they should have, or have incurred fewer claimsthan they should have).

By conducting a multi-condition risk assessment of program participantswithin a defined population, and combining data gathered from themulti-condition risk assessment with predictive modeling and other data,the system and method of the present invention is better able to predictwhich participants would most benefit from some form of preemptivehealthcare intervention, and more efficiently deliver appropriate formsand degrees of healthcare intervention. The drawing FIGURE shows anexample system and method according to the present invention.Preferably, participants in a disease management program are screenedusing one or more claims data filters to identify a defined populationor subgroup of program participants. For example, claims data filtermechanisms according to the present invention may identify programparticipants whose claims data indicate potential risk for a disease orcondition to be monitored (e.g., diabetes, coronary artery disease,congestive heart failure, chronic obstructive pulmonary disease,asthma), a specified level or frequency of claims, and/or otherpotential indicator of increased risk of future uncommon medicalexpense. Additionally or alternatively, the defined population orsubgroup of program participants may include self-referred and/orphysician-referred program participants having indications of one ormore identified conditions or risks. In alternate embodiments of theinvention, all participants in a disease management program are includedin the defined population.

A multi-condition risk assessment is then conducted according to thepresent invention for all or a substantial portion of the definedpopulation of program participants. The multi-condition risk assessmentis preferably carried out independently of any retrospective datacollection based on prior healthcare claims. In this manner, dataelements are collected as part of the multi-condition risk assessment,even for those program participants not yet having incurred claims, orwhose retrospective claims data alone would not indicate a higher riskof increased healthcare needs in the near or longer-term future. Inexample forms of the invention, a preliminary multi-condition riskassessment is conducted for participants upon their entry into a diseasemanagement program. Alternatively or additionally, multi-condition riskassessments are conducted for existing participants, for example on aperiodic basis, upon occurrence of a specified event (e.g., a birthdayor other age-based event, after a set number of months in the program,etc.), upon random selection, at the convenience of the individualparticipant, and/or on some other basis. The multi-condition riskassessment may be carried out via an Internet-based onlinequestion-and-answer session, by telephone questionnaire, in-personinterview, or by filling out a paper questionnaire. The multi-conditionrisk assessment may collect information directly from the participant,or from the participant's designee (e.g., a parent, guardian orcaregiver).

The multi-condition risk assessment preferably includes collection ofdata including one or more medical factors or conditions of anindividual program participant, such as blood pressure (hypertension),cholesterol levels, diabetes, elevated blood-glucose levels, swelling orinflammation, chest pain, fatigue, shortness of breath, depression,cancer, low back pain, cardiac disease (congestive heart failure,coronary artery disease), and/or respiratory disease (asthma, chronicobstructive pulmonary disease). The multi-condition risk assessmentpreferably also includes collection of data relating to one or moremedical treatments of an individual program participant, such asprescription or non-prescription drugs taken, insulin therapy, oxygentherapy, and/or compliance with prescribed treatment regimens.

Preferably, data collected from the multi-condition risk assessments ofindividual program participants are combined with retrospective claimsdata and/or other information to develop a patient record or clinicalprofile for each program participant within the defined population.Preferably, the records are updated continuously or periodically, as forexample with automated claims data feeds, and/or supplementalmulti-condition risk assessment data. Preferably, a combination of datafrom the multi-condition risk assessments and claims data is applied toa statistical model to stratify risk and classify individualparticipants within the defined population into one of two or more riskclassifications. For example, the combination of data may be utilizedfor individual health risk identification purposes such asidentification of undiagnosed conditions and future health risks,monitoring for statistically predicted co-morbidities and potentialcomplications, and/or other means of categorization of future healthcarecost risk severity. Preferably, patient record data are also utilizedfor determining the appropriate level of disease management interventionand support for individual program participants. Preferably, the degreeor intensity of recommended intervention or support increases withincreasing risk severity classification, for example ranging frompatient education for lesser risk individuals, through passivemonitoring and reporting of progress and program compliance for mediumrisk individuals, to more active intervention and close one-on-one casemanagement for higher risk individuals.

Preferably, the patient record data for all program participants arealso aggregated for program analysis and reporting purposes, such asreporting utilization levels, cost savings performance, populationstatistics, financial indicators, etc. The aggregated data from patientrecords are optionally analyzed periodically or continuously to providestatistical feedback on the risk assessment and outcome predictivemodels utilized by the program, and to adjust and optimize those modelsover time based on actual program results.

In preferred and example forms, the system and method of the presentinvention are implemented using a computer based data-management andservices-delivery system, and associated computer software as stored insystem memory and/or on computer-readable media. In particularembodiments, two or more computers within the data management systemcommunicate data over a global communications network such as theInternet. In this manner, the program provider optionally providesautomated data management, scheduling, monitoring, and healthcareeducation and intervention delivery services through online and/orcall-center communication systems. For example, preferably, the computerbased system and method of the present invention receive input dataincluding retrospective claims data and information gathered from amulti-condition risk assessment of program participants. The receiveddata are stored and processed to generate a patient record or clinicalprofile for each program participant in a defined population.Optionally, the profile and/or underlying data are reported to and/orremotely accessed by the participant and/or the participant's doctor orother healthcare provider(s) through secure communications links. Theprofile and/or underlying data are processed to identify undiagnosedmedical conditions, monitor previously identified conditions, and/orclassify future medical risk level.

Based upon the determined risk classification, identified conditions,and/or other information from their profile, the participant may becontacted with customized information regarding proposed treatment,monitoring, recommended lifestyle or behavior changes, and/or otherrisk-mitigation steps, based on clinically accepted practices.Optionally, automated reminders and/or proactive monitoring ofconditions, progress and/or program compliance are generated, and arecommunicated to the participant by email, telephonically by call-centerpersonnel, or otherwise. Preferably, the frequency and content of thesecontacts are customized by the system based on each participant'sindividual profile or risk classification, including severity ofcondition, and the participant's degree of cooperation and compliance.Automated scheduling and reminders of doctor visits, testing andmonitoring, medication delivery and usage, and other activities areoptionally provided to the program participant.

Optionally, the program also provides on-demand counseling and help-lineservices, preferably staffed with specialized healthcare professionalswho can access the participant's profile, and query and advise theparticipant based on system generated information specific to theparticipant. The healthcare professionals may also be prompted toproactively initiate a contact with the participant, for example tomonitor progress or direct program compliance, and/or to support oradvise the participant with respect to particular aspects of their care.

While the invention has been described with reference to preferred andexample embodiments, it will be understood by those skilled in the artthat a variety of modifications, additions and deletions are within thescope of the invention, as defined by the following claims.

1. A method of disease management embodied in a computer program productfor execution on a computer system, comprising: a computer programproduct for execution on an instruction processing system, comprising atangible storage medium readable by the instruction processing systemand storing instructions for execution by the instruction processingsystem for performing the method; filtering claims data of programparticipants to define a population; conducting a multi-condition riskassessment of substantially all participants within the definedpopulation; combining data gathered from said multi-condition riskassessment with claims data for participants within the definedpopulation; classifying future healthcare risk of participants based onthe combination of claims data and data gathered from saidmulti-condition risk assessment; and outputting the classified futurehealthcare risk participants to the tangible storage medium.
 2. Themethod of claim 1, further comprising directing a plan of care based onthe classification of future healthcare risk.
 3. The method of claim 1,wherein the step of conducting a multi-condition risk assessmentcomprises gathering data regarding one or more medical conditions of theprogram participant.
 4. The method of claim 3, wherein the dataregarding one or more medical conditions of the program participant areselected from blood pressure, hypertension, cholesterol levels,diabetes, elevated blood-glucose levels, swelling or inflammation, chestpain, fatigue, shortness of breath, depression, cancer, low back pain,cardiac disease, congestive heart failure, coronary artery disease,respiratory disease, asthma, chronic obstructive pulmonary disease, andcombinations thereof.
 5. The method of claim 1, wherein the step ofconducting a multi-condition risk assessment comprises gathering dataregarding one or more medical treatments of the program participant. 6.The method of claim 5, wherein the data regarding one or more medicaltreatments of the program participant are selected from prescription ornon-prescription drugs taken, insulin therapy, oxygen therapy,compliance with prescribed treatment regimens, and combinations thereof.7. The method of claim 1, wherein a multi-condition risk assessment isconducted for every member of the defined population of programparticipants.
 8. The method of claim 1, wherein the multi-condition riskassessment is a preliminary multi-condition risk assessment conductedupon entry of the participant into a disease management program.
 9. Themethod of claim 1, wherein the multi-condition risk assessment isconducted upon occurrence of a specified event.
 10. The method of claim1, wherein the multi-condition risk assessment is conducted via anInternet-based online question-and-answer session.
 11. The method ofclaim 1, wherein the step of filtering claims data of programparticipants comprises identification of at least one potentialindicator of increased risk of future uncommon medical expense.
 12. Themethod of claim 1, wherein the combination of data from themulti-condition risk assessment and claims data is applied to astatistical model to stratify risk and classify individual participantswithin the defined population into one of two or more riskclassifications.
 13. The method of claim 12, further comprisingdetermining an appropriate level of disease management intervention andsupport for program participants based on their risk classification. 14.The method of claim 1, wherein the defined population further comprisesself-referred and/or physician-referred program participants havingindications of one or more identified conditions or risks.
 15. A methodof disease management embodied in a computer program product forexecution on a computer system comprising: a computer program productfor execution on an instruction processing system, comprising a tangiblestorage medium readable by the instruction processing system and storinginstructions for execution by the instruction processing system forperforming the method; conducting a multi-condition risk assessment ofat least a substantial portion of program participants within a definedpopulation; generating a patient record based on the multi-conditionrisk assessment; and providing differing degrees of healthcareintervention for program participants based on their patient record. 16.The method of claim 15, further comprising collecting retrospectiveclaims data for program participants, and combining said retrospectiveclaims data into the patient records.
 17. A method of optimizingdelivery of healthcare services to participants in a disease managementprogram, said method comprising: a computer program product forexecution on an instruction processing system, comprising a tangiblestorage medium readable by the instruction processing system and storinginstructions for execution by the instruction processing system forperforming the method; conducting a multi-condition risk assessment ofat least a substantial portion of a population; categorizing individualprogram participants selected from the population into at least ahigher-risk category and a lower-risk category; and providing moreintensive healthcare intervention to program participants categorized inthe higher-risk category and less intensive healthcare intervention toprogram participants categorized in the lower-risk category.
 18. Themethod of claim 17, further comprising collecting claims data forprogram participants, and wherein the step of categorizing individualprogram participants selected from the population into at least ahigher-risk category and a lower-risk category comprises combining saidclaims data with data collected by the multi-condition risk assessment.